Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations1257
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory157.2 KiB
Average record size in memory128.1 B

Variable types

Numeric12
Categorical4

Alerts

APACHE II is highly overall correlated with Group and 3 other fieldsHigh correlation
Group is highly overall correlated with APACHE IIHigh correlation
LOS-ICU is highly overall correlated with APACHE II and 1 other fieldsHigh correlation
LymC is highly overall correlated with NLCR and 1 other fieldsHigh correlation
Mortality is highly overall correlated with APACHE II and 1 other fieldsHigh correlation
NLCR is highly overall correlated with LymCHigh correlation
NeuC is highly overall correlated with WBCCHigh correlation
SOFA is highly overall correlated with APACHE II and 2 other fieldsHigh correlation
WBCC is highly overall correlated with LymC and 1 other fieldsHigh correlation
Mortality is highly imbalanced (52.3%) Imbalance
SOFA has 464 (36.9%) zeros Zeros
EOC has 459 (36.5%) zeros Zeros
MPV has 19 (1.5%) zeros Zeros

Reproduction

Analysis started2024-12-19 09:46:48.730595
Analysis finished2024-12-19 09:47:30.981820
Duration42.25 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct81
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.505967
Minimum18
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:31.348750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23
Q141
median58
Q371
95-th percentile86
Maximum99
Range81
Interquartile range (IQR)30

Descriptive statistics

Standard deviation19.235512
Coefficient of variation (CV)0.3404156
Kurtosis-0.85544819
Mean56.505967
Median Absolute Deviation (MAD)15
Skewness-0.16781408
Sum71028
Variance370.00494
MonotonicityNot monotonic
2024-12-19T09:47:31.999181image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 41
 
3.3%
58 35
 
2.8%
63 32
 
2.5%
69 30
 
2.4%
64 29
 
2.3%
51 28
 
2.2%
60 27
 
2.1%
80 27
 
2.1%
62 26
 
2.1%
65 25
 
2.0%
Other values (71) 957
76.1%
ValueCountFrequency (%)
18 7
0.6%
19 13
1.0%
20 10
0.8%
21 8
0.6%
22 16
1.3%
23 15
1.2%
24 13
1.0%
25 11
0.9%
26 14
1.1%
27 13
1.0%
ValueCountFrequency (%)
99 1
 
0.1%
98 3
 
0.2%
97 1
 
0.1%
96 3
 
0.2%
95 1
 
0.1%
94 6
0.5%
92 1
 
0.1%
91 1
 
0.1%
90 5
0.4%
89 8
0.6%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size9.9 KiB
E
744 
K
512 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1256
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowK
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E 744
59.2%
K 512
40.7%
(Missing) 1
 
0.1%

Length

2024-12-19T09:47:32.494549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T09:47:32.900796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
e 744
59.2%
k 512
40.8%

Most occurring characters

ValueCountFrequency (%)
E 744
59.2%
K 512
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 744
59.2%
K 512
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 744
59.2%
K 512
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 744
59.2%
K 512
40.8%

Diagnosis
Categorical

Distinct3
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size9.9 KiB
EC
616 
M
594 
AC
 
46

Length

Max length2
Median length2
Mean length1.5270701
Min length1

Characters and Unicode

Total characters1918
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowEC

Common Values

ValueCountFrequency (%)
EC 616
49.0%
M 594
47.3%
AC 46
 
3.7%
(Missing) 1
 
0.1%

Length

2024-12-19T09:47:33.378107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T09:47:33.784397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ec 616
49.0%
m 594
47.3%
ac 46
 
3.7%

Most occurring characters

ValueCountFrequency (%)
C 662
34.5%
E 616
32.1%
M 594
31.0%
A 46
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 662
34.5%
E 616
32.1%
M 594
31.0%
A 46
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 662
34.5%
E 616
32.1%
M 594
31.0%
A 46
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 662
34.5%
E 616
32.1%
M 594
31.0%
A 46
 
2.4%

APACHE II
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.336516
Minimum0
Maximum48
Zeros6
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:34.047641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median11
Q318
95-th percentile30
Maximum48
Range48
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.2097626
Coefficient of variation (CV)0.61558528
Kurtosis0.70790712
Mean13.336516
Median Absolute Deviation (MAD)5
Skewness0.99969371
Sum16764
Variance67.400202
MonotonicityNot monotonic
2024-12-19T09:47:34.364070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
5 102
 
8.1%
10 91
 
7.2%
9 84
 
6.7%
7 78
 
6.2%
8 70
 
5.6%
16 62
 
4.9%
11 59
 
4.7%
14 58
 
4.6%
12 53
 
4.2%
18 51
 
4.1%
Other values (35) 549
43.7%
ValueCountFrequency (%)
0 6
 
0.5%
1 6
 
0.5%
2 8
 
0.6%
3 44
3.5%
4 50
4.0%
5 102
8.1%
6 47
3.7%
7 78
6.2%
8 70
5.6%
9 84
6.7%
ValueCountFrequency (%)
48 1
 
0.1%
47 1
 
0.1%
46 1
 
0.1%
44 1
 
0.1%
42 1
 
0.1%
39 2
0.2%
38 3
0.2%
37 1
 
0.1%
36 4
0.3%
35 3
0.2%

SOFA
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5823389
Minimum0
Maximum16
Zeros464
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:34.639748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile10
Maximum16
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2538228
Coefficient of variation (CV)1.2600294
Kurtosis1.7818887
Mean2.5823389
Median Absolute Deviation (MAD)1
Skewness1.5206715
Sum3246
Variance10.587363
MonotonicityNot monotonic
2024-12-19T09:47:34.924164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 464
36.9%
2 197
15.7%
1 171
 
13.6%
3 88
 
7.0%
4 61
 
4.9%
5 58
 
4.6%
6 47
 
3.7%
7 42
 
3.3%
8 36
 
2.9%
10 26
 
2.1%
Other values (7) 67
 
5.3%
ValueCountFrequency (%)
0 464
36.9%
1 171
 
13.6%
2 197
15.7%
3 88
 
7.0%
4 61
 
4.9%
5 58
 
4.6%
6 47
 
3.7%
7 42
 
3.3%
8 36
 
2.9%
9 21
 
1.7%
ValueCountFrequency (%)
16 1
 
0.1%
15 4
 
0.3%
14 5
 
0.4%
13 6
 
0.5%
12 16
 
1.3%
11 14
 
1.1%
10 26
2.1%
9 21
1.7%
8 36
2.9%
7 42
3.3%

CRP
Real number (ℝ)

Distinct609
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1850835
Minimum0
Maximum52.05
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:35.228627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median3
Q38
95-th percentile24.844
Maximum52.05
Range52.05
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation8.241324
Coefficient of variation (CV)1.3324515
Kurtosis6.1042033
Mean6.1850835
Median Absolute Deviation (MAD)2.6
Skewness2.2788473
Sum7774.65
Variance67.919421
MonotonicityNot monotonic
2024-12-19T09:47:35.568260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 42
 
3.3%
0.4 42
 
3.3%
0.3 38
 
3.0%
0.1 34
 
2.7%
0.5 33
 
2.6%
0.7 28
 
2.2%
1.2 27
 
2.1%
1 25
 
2.0%
0.6 22
 
1.8%
0.9 18
 
1.4%
Other values (599) 948
75.4%
ValueCountFrequency (%)
0 1
 
0.1%
0.01 2
 
0.2%
0.02 4
0.3%
0.03 3
0.2%
0.04 2
 
0.2%
0.05 4
0.3%
0.06 4
0.3%
0.07 5
0.4%
0.08 4
0.3%
0.09 4
0.3%
ValueCountFrequency (%)
52.05 1
0.1%
51.5 1
0.1%
50.53 1
0.1%
50.1 1
0.1%
47.46 1
0.1%
46.2 1
0.1%
45.8 1
0.1%
44.2 1
0.1%
43.4 1
0.1%
42.62 1
0.1%

WBCC
Real number (ℝ)

High correlation 

Distinct925
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.767942
Minimum0.6
Maximum51.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:35.901439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile4.032
Q17.74
median10.79
Q314.89
95-th percentile22.19
Maximum51.08
Range50.48
Interquartile range (IQR)7.15

Descriptive statistics

Standard deviation5.9623405
Coefficient of variation (CV)0.50665958
Kurtosis4.2449817
Mean11.767942
Median Absolute Deviation (MAD)3.54
Skewness1.3837783
Sum14792.303
Variance35.549504
MonotonicityNot monotonic
2024-12-19T09:47:36.229917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.4 4
 
0.3%
9.81 4
 
0.3%
10.63 4
 
0.3%
13.83 4
 
0.3%
8.67 4
 
0.3%
10.64 4
 
0.3%
8.58 4
 
0.3%
8.03 4
 
0.3%
8.85 4
 
0.3%
10.24 3
 
0.2%
Other values (915) 1218
96.9%
ValueCountFrequency (%)
0.6 1
0.1%
0.61 1
0.1%
0.75 1
0.1%
0.78 1
0.1%
0.93 1
0.1%
0.99 1
0.1%
1.02 1
0.1%
1.37 1
0.1%
1.88 2
0.2%
1.98 1
0.1%
ValueCountFrequency (%)
51.08 1
0.1%
46.32 1
0.1%
45.59 1
0.1%
37.36 1
0.1%
36.95 1
0.1%
36.91 1
0.1%
36.52 1
0.1%
36.21 1
0.1%
35.98 1
0.1%
33.74 1
0.1%

NeuC
Real number (ℝ)

High correlation 

Distinct893
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9004972
Minimum0.2
Maximum50.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:36.558853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile3.028
Q16.19
median9.02
Q312.73
95-th percentile19.12
Maximum50.5
Range50.3
Interquartile range (IQR)6.54

Descriptive statistics

Standard deviation5.3388792
Coefficient of variation (CV)0.53925364
Kurtosis5.1924321
Mean9.9004972
Median Absolute Deviation (MAD)3.25
Skewness1.4794939
Sum12444.925
Variance28.503631
MonotonicityNot monotonic
2024-12-19T09:47:36.912960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.4 5
 
0.4%
5.73 5
 
0.4%
7.66 5
 
0.4%
8.65 5
 
0.4%
9.07 5
 
0.4%
5.85 4
 
0.3%
7.95 4
 
0.3%
7.83 4
 
0.3%
7.18 4
 
0.3%
7.93 4
 
0.3%
Other values (883) 1212
96.4%
ValueCountFrequency (%)
0.2 1
0.1%
0.28 1
0.1%
0.35 1
0.1%
0.44 1
0.1%
0.6 1
0.1%
0.72 1
0.1%
0.81 1
0.1%
0.92 1
0.1%
1.07 1
0.1%
1.21 1
0.1%
ValueCountFrequency (%)
50.5 1
0.1%
40.56 1
0.1%
39.59 1
0.1%
35.41 1
0.1%
34.17 1
0.1%
32.23 1
0.1%
31.21 1
0.1%
29.99 1
0.1%
29.63 1
0.1%
29 1
0.1%

LymC
Real number (ℝ)

High correlation 

Distinct264
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0345195
Minimum0.005
Maximum6.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:37.218977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.25
Q10.55
median0.86
Q31.28
95-th percentile2.358
Maximum6.97
Range6.965
Interquartile range (IQR)0.73

Descriptive statistics

Standard deviation0.75575338
Coefficient of variation (CV)0.73053566
Kurtosis10.101207
Mean1.0345195
Median Absolute Deviation (MAD)0.35
Skewness2.4827214
Sum1300.391
Variance0.57116317
MonotonicityNot monotonic
2024-12-19T09:47:37.583659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.46 22
 
1.8%
0.58 20
 
1.6%
0.61 18
 
1.4%
0.6 15
 
1.2%
0.76 15
 
1.2%
0.62 15
 
1.2%
0.63 15
 
1.2%
0.49 14
 
1.1%
0.95 14
 
1.1%
0.75 14
 
1.1%
Other values (254) 1095
87.1%
ValueCountFrequency (%)
0.005 1
 
0.1%
0.04 1
 
0.1%
0.07 1
 
0.1%
0.1 1
 
0.1%
0.11 3
0.2%
0.12 2
0.2%
0.13 2
0.2%
0.14 2
0.2%
0.15 3
0.2%
0.16 2
0.2%
ValueCountFrequency (%)
6.97 1
0.1%
5.8 1
0.1%
5.54 1
0.1%
5.38 1
0.1%
5.29 1
0.1%
5.03 1
0.1%
4.75 1
0.1%
4.62 2
0.2%
4.6 1
0.1%
4.53 1
0.1%

EOC
Real number (ℝ)

Zeros 

Distinct29
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.239459
Minimum0
Maximum410
Zeros459
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:37.919297image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q340
95-th percentile100
Maximum410
Range410
Interquartile range (IQR)40

Descriptive statistics

Standard deviation42.282799
Coefficient of variation (CV)1.5522628
Kurtosis14.641156
Mean27.239459
Median Absolute Deviation (MAD)10
Skewness3.0886798
Sum34240
Variance1787.8351
MonotonicityNot monotonic
2024-12-19T09:47:38.198261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 459
36.5%
10 273
21.7%
20 125
 
9.9%
30 75
 
6.0%
60 59
 
4.7%
40 58
 
4.6%
80 39
 
3.1%
50 37
 
2.9%
70 32
 
2.5%
90 29
 
2.3%
Other values (19) 71
 
5.6%
ValueCountFrequency (%)
0 459
36.5%
10 273
21.7%
20 125
 
9.9%
30 75
 
6.0%
40 58
 
4.6%
50 37
 
2.9%
60 59
 
4.7%
70 32
 
2.5%
80 39
 
3.1%
90 29
 
2.3%
ValueCountFrequency (%)
410 1
0.1%
360 1
0.1%
320 1
0.1%
280 1
0.1%
250 1
0.1%
240 1
0.1%
230 2
0.2%
220 2
0.2%
210 2
0.2%
200 2
0.2%

NLCR
Real number (ℝ)

High correlation 

Distinct1211
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.116075
Minimum0.48141264
Maximum420.83333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:38.499383image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.48141264
5-th percentile3.2988235
Q16.8834356
median10.478261
Q315.517241
95-th percentile28.731113
Maximum420.83333
Range420.35192
Interquartile range (IQR)8.6338058

Descriptive statistics

Standard deviation15.289946
Coefficient of variation (CV)1.165741
Kurtosis405.86527
Mean13.116075
Median Absolute Deviation (MAD)4.0420907
Skewness16.081701
Sum16486.907
Variance233.78245
MonotonicityNot monotonic
2024-12-19T09:47:38.831382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 6
 
0.5%
12 3
 
0.2%
22.76086957 3
 
0.2%
11 3
 
0.2%
16.33333333 2
 
0.2%
6 2
 
0.2%
18.34545455 2
 
0.2%
9.6 2
 
0.2%
4.581632653 2
 
0.2%
24.32786885 2
 
0.2%
Other values (1201) 1230
97.9%
ValueCountFrequency (%)
0.4814126394 1
0.1%
0.8694736842 1
0.1%
0.9884726225 1
0.1%
1.09375 1
0.1%
1.152380952 1
0.1%
1.217391304 1
0.1%
1.251162791 1
0.1%
1.294117647 1
0.1%
1.409937888 1
0.1%
1.670634921 1
0.1%
ValueCountFrequency (%)
420.8333333 1
0.1%
111.5 1
0.1%
106.0909091 1
0.1%
86.38095238 1
0.1%
75 1
0.1%
74.9 1
0.1%
68.05 1
0.1%
67 1
0.1%
62.25 1
0.1%
62.2 1
0.1%

PLTC
Real number (ℝ)

Distinct387
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.36197
Minimum11
Maximum854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:39.138825image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile48
Q1120
median184
Q3247
95-th percentile392
Maximum854
Range843
Interquartile range (IQR)127

Descriptive statistics

Standard deviation107.59393
Coefficient of variation (CV)0.55357501
Kurtosis2.916258
Mean194.36197
Median Absolute Deviation (MAD)64
Skewness1.1856583
Sum244313
Variance11576.454
MonotonicityNot monotonic
2024-12-19T09:47:39.454974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214 12
 
1.0%
147 12
 
1.0%
189 11
 
0.9%
196 9
 
0.7%
188 9
 
0.7%
158 9
 
0.7%
168 9
 
0.7%
169 9
 
0.7%
224 9
 
0.7%
152 9
 
0.7%
Other values (377) 1159
92.2%
ValueCountFrequency (%)
11 1
0.1%
12 1
0.1%
13 1
0.1%
14 2
0.2%
16 1
0.1%
18 2
0.2%
19 1
0.1%
23 2
0.2%
24 1
0.1%
26 2
0.2%
ValueCountFrequency (%)
854 1
0.1%
750 1
0.1%
665 1
0.1%
664 1
0.1%
655 1
0.1%
613 1
0.1%
607 1
0.1%
590 2
0.2%
579 1
0.1%
578 1
0.1%

MPV
Real number (ℝ)

Zeros 

Distinct63
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.127741
Minimum0
Maximum107
Zeros19
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:39.779208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.6
Q19.4
median10
Q310.8
95-th percentile11.9
Maximum107
Range107
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation3.7224214
Coefficient of variation (CV)0.36754707
Kurtosis465.22193
Mean10.127741
Median Absolute Deviation (MAD)0.7
Skewness19.076686
Sum12730.57
Variance13.856421
MonotonicityNot monotonic
2024-12-19T09:47:40.119486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 86
 
6.8%
9.6 55
 
4.4%
10.1 54
 
4.3%
10.7 49
 
3.9%
10.2 47
 
3.7%
10.4 47
 
3.7%
11 46
 
3.7%
9.9 46
 
3.7%
9.4 44
 
3.5%
10.6 42
 
3.3%
Other values (53) 741
58.9%
ValueCountFrequency (%)
0 19
1.5%
7.6 1
 
0.1%
7.7 1
 
0.1%
7.9 2
 
0.2%
8 3
 
0.2%
8.1 6
 
0.5%
8.2 6
 
0.5%
8.3 7
 
0.6%
8.4 3
 
0.2%
8.5 13
1.0%
ValueCountFrequency (%)
107 1
 
0.1%
80.2 1
 
0.1%
13.7 1
 
0.1%
13.5 1
 
0.1%
13.4 2
 
0.2%
13.3 1
 
0.1%
13.1 1
 
0.1%
13 1
 
0.1%
12.9 1
 
0.1%
12.7 5
0.4%

Group
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
0
816 
1
441 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1257
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 816
64.9%
1 441
35.1%

Length

2024-12-19T09:47:40.449919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T09:47:40.730785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 816
64.9%
1 441
35.1%

Most occurring characters

ValueCountFrequency (%)
0 816
64.9%
1 441
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 816
64.9%
1 441
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 816
64.9%
1 441
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 816
64.9%
1 441
35.1%

LOS-ICU
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4359586
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-12-19T09:47:41.016045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum96
Range95
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.5263312
Coefficient of variation (CV)1.9220944
Kurtosis36.265996
Mean4.4359586
Median Absolute Deviation (MAD)0
Skewness5.2049994
Sum5576
Variance72.698324
MonotonicityNot monotonic
2024-12-19T09:47:41.336332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 631
50.2%
2 182
 
14.5%
3 104
 
8.3%
4 64
 
5.1%
5 60
 
4.8%
7 27
 
2.1%
6 21
 
1.7%
9 17
 
1.4%
10 15
 
1.2%
8 14
 
1.1%
Other values (40) 122
 
9.7%
ValueCountFrequency (%)
1 631
50.2%
2 182
 
14.5%
3 104
 
8.3%
4 64
 
5.1%
5 60
 
4.8%
6 21
 
1.7%
7 27
 
2.1%
8 14
 
1.1%
9 17
 
1.4%
10 15
 
1.2%
ValueCountFrequency (%)
96 1
0.1%
92 1
0.1%
84 1
0.1%
61 1
0.1%
59 1
0.1%
58 1
0.1%
55 2
0.2%
54 1
0.1%
52 1
0.1%
51 1
0.1%

Mortality
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
0
1128 
1
129 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1257
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1128
89.7%
1 129
 
10.3%

Length

2024-12-19T09:47:41.615143image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T09:47:41.867896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1128
89.7%
1 129
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 1128
89.7%
1 129
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1128
89.7%
1 129
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1128
89.7%
1 129
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1128
89.7%
1 129
 
10.3%

Interactions

2024-12-19T09:47:25.752811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:49.836277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:53.312144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:56.123493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:00.004901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:03.733660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:06.786725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:09.654749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:12.586002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:16.628299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:20.239464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:22.955537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:26.014904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:50.121683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:53.565526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:56.387708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:00.382653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:03.991045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:07.036798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:09.926842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:12.840486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:17.321819image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:20.476496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:23.224519image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:26.282954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:50.387761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:53.788573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:56.650518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:00.679719image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:04.212638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:07.278481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:10.161821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:13.126301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:17.683520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:20.700794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:23.445206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:26.518629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:50.783281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:54.027986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:56.901541image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:01.023558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:04.456468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:07.523327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:10.410576image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:13.488583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:18.068907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:20.947946image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:23.697269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:26.739976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:51.205372image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:54.256174image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:57.131061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:01.347218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:04.692765image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:07.777861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:10.652643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:13.775900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:18.430522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:21.164487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:23.930060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:26.964490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:51.465930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:54.519111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:57.571986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:01.693360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:04.916792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:07.992785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:10.885042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:14.089559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:18.661027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:21.380336image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:24.172312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:27.203069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:51.881002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:54.747533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:57.900252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:02.056802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:05.160770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:08.238975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:11.121004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:14.471236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:18.893731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:21.608246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:24.402699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:27.441504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:52.125921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:54.987399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:58.270401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:02.443139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:05.392697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:08.494886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:11.393340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:14.859090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:19.126281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:21.841781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:24.653930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:27.651892image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:52.375935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:55.195420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:58.626266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:02.754966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:05.637937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:08.725857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:11.634815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:15.174762image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:19.344421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:22.063258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:24.872818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:27.870121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:52.607947image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:55.409188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:58.928090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:03.052796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:05.860517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:08.941589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:11.864862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:15.496983image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:19.552336image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:22.270620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:25.105997image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:28.088375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:52.841022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:55.655941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:59.266542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:03.267062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:06.093627image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:09.153540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:12.084186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:15.843025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:19.799236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:22.483863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:25.314798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:28.322853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:53.071796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:55.886208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:46:59.632965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:03.499088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:06.566952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:09.403075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:12.310479image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:16.258158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:20.022280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:22.712930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:47:25.530881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-19T09:47:42.050144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
APACHE IIAgeCRPDiagnosisEOCGenderGroupLOS-ICULymCMPVMortalityNLCRNeuCPLTCSOFAWBCC
APACHE II1.0000.4800.3720.4270.1670.0850.5100.537-0.1770.0560.5930.070-0.112-0.1280.666-0.108
Age0.4801.0000.3580.2090.1300.1340.2100.274-0.1240.0490.1050.025-0.0910.0170.286-0.095
CRP0.3720.3581.0000.1820.1280.0450.2940.331-0.062-0.0100.1560.0540.0170.0950.3570.027
Diagnosis0.4270.2090.1821.0000.1400.0000.4870.2130.0430.0290.3250.0430.1170.0700.3610.114
EOC0.1670.1300.1280.1401.0000.0420.0000.1460.193-0.0340.106-0.294-0.1420.1120.162-0.086
Gender0.0850.1340.0450.0000.0421.0000.0000.0000.0970.0000.0140.0000.0000.0990.0840.000
Group0.5100.2100.2940.4870.0000.0001.0000.3660.1450.0000.3190.1210.1540.1600.4290.156
LOS-ICU0.5370.2740.3310.2130.1460.0000.3661.000-0.108-0.0400.2440.026-0.074-0.0060.544-0.071
LymC-0.177-0.124-0.0620.0430.1930.0970.145-0.1081.000-0.0780.058-0.6350.4000.420-0.1360.510
MPV0.0560.049-0.0100.029-0.0340.0000.000-0.040-0.0781.0000.0000.050-0.031-0.3240.059-0.041
Mortality0.5930.1050.1560.3250.1060.0140.3190.2440.0580.0001.0000.0790.1450.0880.6230.085
NLCR0.0700.0250.0540.043-0.2940.0000.1210.026-0.6350.0500.0791.0000.378-0.1000.0070.257
NeuC-0.112-0.0910.0170.117-0.1420.0000.154-0.0740.400-0.0310.1450.3781.0000.374-0.1330.980
PLTC-0.1280.0170.0950.0700.1120.0990.160-0.0060.420-0.3240.088-0.1000.3741.000-0.2080.414
SOFA0.6660.2860.3570.3610.1620.0840.4290.544-0.1360.0590.6230.007-0.133-0.2081.000-0.125
WBCC-0.108-0.0950.0270.114-0.0860.0000.156-0.0710.510-0.0410.0850.2570.9800.414-0.1251.000

Missing values

2024-12-19T09:47:29.184385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-19T09:47:30.098890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-19T09:47:30.732150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AgeGenderDiagnosisAPACHE IISOFACRPWBCCNeuCLymCEOCNLCRPLTCMPVGroupLOS-ICUMortality
046EM1600.1812.2210.530.9805010.7448981649.7070
146EM700.1514.9211.912.120105.6179252419.4010
233KM321039.190.600.200.005040.000000148.91541
376EM25417.8114.8213.492.800204.8178572829.0030
465EEC14027.558.187.150.4104017.43902415211.6020
580EM1512.1215.4012.591.280209.83593828011.0110
623EAC1400.0210.488.661.220407.09836117610.0010
753EAC1039.0019.1616.280.620026.25806528010.8010
881EM1102.0115.3113.581.200011.31666717710.9010
959KM2104.658.957.970.620012.8548392999.30150
AgeGenderDiagnosisAPACHE IISOFACRPWBCCNeuCLymCEOCNLCRPLTCMPVGroupLOS-ICUMortality
124767EM3279.17.295.580.378015.0810811189.01171
124852EEC700.116.7113.921.63108.53987716010.6010
124959EEC1022.85.754.820.4610010.4782611209.7010
125066EM34115.215.5112.271.4408.52083319010.6111
125127EEC500.119.6315.842.05107.7268291948.8010
125241EEC720.63.352.880.30109.6000004611.4010
125363EM1465.89.808.320.761010.94736813211.3130
125460EM2481.25.064.380.49108.9387762568.0031
125579EEC1543.321.2319.540.421046.52381014510.6010
125631KM1652.08.406.690.76208.8026321649.0140